Image-Guided Navigation in Spine Surgery: From Historical Developments to Future Perspectives.

Intraoperative navigation is critical during spine surgery to ensure accurate instrumentation placement. From the early era of fluoroscopy to the current advancement in robotics, spinal navigation has continued to evolve. By understanding the variations in system protocols and their respective usage in the operating room, the surgeon can use and maximize the potential of various image guidance options more effectively. At the same time, maintaining navigation accuracy throughout the procedure is of the utmost importance, which can be confirmed intraoperatively by using an internal fiducial marker, as demonstrated herein. This technology can reduce the need for revision surgeries, minimize postoperative complications, and enhance the overall efficiency of operating rooms.

Parkinsonism originates in a discrete secondary and dystonia in a primary motor cortical-basal ganglia subcircuit.

Although manifesting contrasting phenotypes, Parkinson's disease and dystonia, the two most common movement disorders, can originate from similar pathophysiology. Previously, we demonstrated that lesioning (silencing) of a discrete dorsal region in the globus pallidus (rodent equivalent to globus pallidus externa) in rats and produced parkinsonism, while lesioning a nearby ventral hotspot-induced dystonia. Presently, we injected fluorescent-tagged multi-synaptic tracers into these pallidal hotspots (n = 36 Long Evans rats) and permitted 4 days for the viruses to travel along restricted connecting pathways and reach the motor cortex before sacrificing the animals. Viral injections in the Parkinson's hotspot fluorescent labeled a circumscribed region in the secondary motor cortex, while injections in the dystonia hotspot labeled within the primary motor cortex. Custom probability mapping and N200 staining affirmed the segregation of the cortical territories for Parkinsonism and dystonia to the secondary and primary motor cortices. Intracortical microstimulation localized territories specifically to their respective rostral and caudal microexcitable zones. Parkinsonian features are thus explained by pathological signaling within a secondary motor subcircuit normally responsible for initiation and scaling of movement, while dystonia is explained by abnormal (and excessive) basal ganglia signaling directed at primary motor corticospinal transmission.

Deep Brain Stimulation of Nucleus Basalis of Meynert improves learning in rat model of dementia.

Background: Deep brain stimulation (DBS) of the nucleus basalis of Meynert (NBM) has been preliminarily investigated as a potential treatment for dementia. The degeneration of NBM cholinergic neurons is a pathological feature of many forms of dementia. Although stimulation of the NBM has been demonstrated to improve learning, the ideal parameters for NBM stimulation have not been elucidated. This study assesses the differential effects of varying stimulation patterns and duration on learning in a dementia rat model. Methods: 192-IgG-saporin (or vehicle) was injected into the NBM to produce dementia in rats. Next, all rats underwent unilateral implantation of a DBS electrode in the NBM. The experimental groups consisted of i-normal, ii-untreated demented, and iii-demented rats receiving NBM DBS. The stimulation paradigms included testing different modes (tonic and burst) and durations (1-hr, 5-hrs, and 24-hrs/day) over 10 daily sessions. Memory was assessed pre- and post-stimulation using two established learning paradigms: novel object recognition (NOR) and auditory operant chamber learning. Results: Both normal and stimulated rats demonstrated improved performance in NOR and auditory learning as compared to the unstimulated demented group. The burst stimulation groups performed better than the tonic stimulated group. Increasing the daily stimulation duration to 24-hr did not further improve cognitive performance in an auditory recognition task and degraded the results on a NOR task as compared with 5-hr. Conclusion: The present findings suggest that naturalistic NBM burst DBS may offer a potential effective therapy for treating dementia and suggests potential strategies for the reevaluation of current human NBM stimulation paradigms.

Application of Machine Learning Strategies to Model the Effects of Sevoflurane on Somatosensory-Evoked Potentials during Spine Surgery.

In this study, a small sample of patients' neuromonitoring data was analyzed using machine learning (ML) tools to provide proof of concept for quantifying complex signals. Intraoperative neurophysiological monitoring (IONM) is a valuable asset for monitoring the neurological status of a patient during spine surgery. Notably, this technology, when operated by neurophysiologists and surgeons familiar with proper alarm criteria, is capable of detecting neurological deficits. However, non-surgical factors, such as volatile anesthetics like sevoflurane, can negatively influence robust IONM signal generation. While sevoflurane has been shown to affect the latency and amplitude of somatosensory evoked potential (SSEP), a more complex and nuanced analysis of the SSEP waveform has not been performed. In this study, signal processing and machine learning techniques were used to more intricately characterize and predict SSEP waveform changes as a function of varying end-tidal sevoflurane concentration. With data from ten patients who underwent spinal procedures, features describing the SSEP waveforms were generated using principal component analysis (PCA), phase space curves (PSC), and time-frequency analysis (TFA). A minimum redundancy maximum relevance (MRMR) feature selection technique was then used to identify the most important SSEP features associated with changing sevoflurane concentrations. Once the features carrying the maximum amount of information about the majority of signal waveform variability were identified, ML models were used to predict future changes in SSEP waveforms. Linear regression, regression trees, support vector machines, and neural network ML models were then selected for testing. Using SSEP data from eight patients, the models were trained using a range of features selected during MRMR calculations. During the training phase of model development, the highest performing models were identified as support vector machines and regression trees. After identifying the highest performing models for each nerve group, we tested these models using the remaining two patients' data. We compared the models' performance metrics using the root mean square error values (RMSEs). The feasibility of the methodology described provides a general framework for the applications of machine learning strategies to further delineate the effects of surgical and non-surgical factors affecting IONM signals.

Healthy and pathological pallidal regulation of thalamic burst versus tonic mode firing: a computational simulation.

The mechanisms by which the basal ganglia influence the pallidal-receiving thalamus remain to be adequately defined. Our prior in vivo recordings in fully alert normal and dystonic rats revealed that normally fast tonic discharging entopeduncular [EP, rodent equivalent of the globus pallidus internus (GPi)] neurons are pathologically slow, highly irregular, and bursty under dystonic conditions. This, in turn, induces pallidal-receiving thalamic movement-related neurons to change from a healthy burst predominant to a pathological tonic-predominant resting firing mode. This study aims to understand the pallidal influence on thalamic firing modes using computational simulations. We inputted various combinations of healthy and pathological (dystonic) in vivo neuronal recordings to the Rubin and Terman's computational model of low threshold spiking pallidothalamic neurons. The input sets consist of representative tonic, burst, irregular tonic and irregular burst inputs collected from EP/GPi in our animal lab. Initial test combinations of EP/ GPi input to the model were identical to the neuronal population distributions observed in vivo. The thalamic neuron model outputted similar firing rate and mode as observed in corresponding in-vivo thalamus. Further influence of each individual patterns was also delineated. By simulating the firing properties of encountered neurons, the basal ganglia output is suggested to critically act as firing mode selector for thalamic motor relay neurons. By selecting and determining the timing and extent of opening of thalamic T-type calcium channels via GABAergic hyperpolarizing input, GPi neurons are in position to precisely orchestrate thalamocortical burst motor signaling.

The Use of Intraoperative Neuromonitoring for Cervical Spine Surgery: Indications, Challenges, and Advances.

Intraoperative neuromonitoring (IONM) has become an indispensable surgical adjunct in cervical spine procedures to minimize surgical complications. Understanding the historical development of IONM, indications for use, associated pitfalls, and recent developments will allow the surgeon to better utilize this important technology. While IONM has shown great promise in procedures for cervical deformity, intradural tumors, or myelopathy, routine use in all cervical spine cases with moderate pathology remains controversial. Pitfalls that need to be addressed include human error, a lack of efficient communication, variable alarm warning criteria, and a non-standardized checklist protocol. As the techniques associated with IONM technology become more robust moving forward, IONM emerges as a crucial solution to updating patient safety protocols.

Transcranial Stimulation for the Treatment of Stimulant Use Disorder.

The increasing prevalence of stimulant use disorder (StUD) involving methamphetamine and cocaine has been a growing healthcare concern in the United States. Cocaine usage is associated with atherosclerosis, systolic and diastolic dysfunction, and arrhythmias. Furthermore, approximately one of every four MIs is cocaine-induced among patients aged 18 to 45. Methamphetamine use has been associated with nerve terminal damage in the dopaminergic system resulting in impaired motor function, cognitive decline, and co-morbid psychiatric disorders. Current treatment options for StUD are extremely limited, and there are currently no FDA-approved pharmacotherapies. Behavioral interventions are considered first-line treatment; however, in a recent meta-analysis comparing behavioral treatment options for cocaine, contingency management programs provided the only significant reduction in use. Current evidence points to the potential of various neuromodulation techniques as the next best modality in treating StUD. The most promising evidence thus far has been transcranial magnetic stimulation which several studies have shown to reduce risk factors associated with relapse. Another more invasive neuromodulation technique being studied is deep-brain stimulation, which has shown promising results in its ability to modulate reward circuits to treat addiction. Results showing the impact of transcranial magnetic stimulation (TMS) in the treatment of StUD are limited by the lack of studies conducted and the limited understanding of the neurological involvement driving addiction-based diseases such as StUD. Future studies should seek to provide data on consumption-reducing effects rather than craving evaluations.

Nucleus Basalis of Meynert Stimulation for Dementia: Theoretical and Technical Considerations.

Deep brain stimulation (DBS) of nucleus basalis of Meynert (NBM) is currently being evaluated as a potential therapy to improve memory and overall cognitive function in dementia. Although, the animal literature has demonstrated robust improvement in cognitive functions, phase 1 trial results in humans have not been as clear-cut. We hypothesize that this may reflect differences in electrode location within the NBM, type and timing of stimulation, and the lack of a biomarker for determining the stimulation's effectiveness in real time. In this article, we propose a methodology to address these issues in an effort to effectively interface with this powerful cognitive nucleus for the treatment of dementia. Specifically, we propose the use of diffusion tensor imaging to identify the nucleus and its tracts, quantitative electroencephalography (QEEG) to identify the physiologic response to stimulation during programming, and investigation of stimulation parameters that incorporate the phase locking and cross frequency coupling of gamma and slower oscillations characteristic of the NBM's innate physiology. We propose that modulating the baseline gamma burst stimulation frequency, specifically with a slower rhythm such as theta or delta will pose more effective coupling between NBM and different cortical regions involved in many learning processes.

Parkinsonism and dystonia are differentially induced by modulation of different territories in the basal ganglia.

Numerous clinical and experimental observations suggest that deficient neuronal signaling in the globus pallidus externa (GPe) is integral to both Parkinson's disease (PD) and dystonia. In our previous studies in jaundiced dystonic rats, widespread silencing of neurons in GP (rodent equivalent to GPe) preceded and persisted during dystonic motor activity. We therefore hypothesized that on a background of slow and highly irregular and bursty neuronal activity in GP, cortical motor drive produces profound inhibition of GP as the basis for action-induced dystonia in Gunn rats. Presently, the neurotoxin ibotenate was injected locally into the motor territory of GP at one to four sites, over one to two tracts, in 19 normal rats. We found that highly circumscribed dorsal motor territory lesions reproducibly induced parkinsonism, while ventral lesions consistently produced dystonia. Post-lesioning, slow neuronal burst oscillations in the entopeduncular nucleus distinguished parkinsonian from dystonic rats. Next, we compared the deep brain stimulation contact sites in the GP internus used to treat patients with PD (n=21 implants in 12 successive patients) versus dystonia (n=16 implants in nine patients) and found the efficacious territory for ameliorating PD to be located chiefly dorsal to that for dystonia. The comparative distribution for treating PD versus dystonia was therefore anatomically consistent with that for inducing these features via GP lesions in rodents. Our collective findings thus suggest that dystonia and parkinsonism are differentially produced by pathological silencing of GPe neurons along distinct motor sub-circuits, resulting in disparate pathological basal ganglia output signaling.

Preserved dichotomy but highly irregular and burst discharge in the basal ganglia in alert dystonic rats at rest.

Despite its prevalence, the underlying pathophysiology of dystonia remains poorly understood. Using our novel tri-component classification algorithm, extracellular neuronal activity in the globus pallidus (GP), STN, and the entopeduncular nucleus (EP) was characterized in 34 normal and 25 jaundiced dystonic Gunn rats with their heads restrained while at rest. In normal rats, neurons in each nucleus were similarly characterized by two physiologically distinct types: regular tonic with moderate discharge frequencies (mean rates in GP, STN and EP ranging from 35-41 spikes/s) or irregular at slower frequencies (17-20 spikes/s), with a paucity of burst activity. In dystonic rats, these nuclei were also characterized by two distinct principal neuronal patterns. However, in marked difference, in the dystonic rats, neurons were primarily slow and highly irregular (12-15 spikes/s) or burst predominant (14-17 spikes/s), with maintained modest differences between nuclei. In GP and EP, with increasing severity of dystonia, burstiness was moderately further increased, irregularity mildly further increased, and discharge rates mildly further reduced. In contrast, these features did not appreciably change in STN with worsening dystonia. Findings of a lack of bursting in GP, STN and EP in normal rats in an alert resting state and prominent bursting in dystonic Gunn rats suggest that cortical or other external drive is normally required for bursting in these nuclei and that spontaneous bursting, as seen in dystonia and Parkinson's disease, is reflective of an underlying pathophysiological state. Moreover, the extent of burstiness appears to most closely correlate with the severity of the dystonia.

A novel tri-component scheme for classifying neuronal discharge patterns.

BACKGROUND: Neuronal discharge patterns can be described by three principle patterns, namely, regular, irregular, and bursty. NEW METHOD: Available discrimination metrics, including global ISI metrics (e.g., coefficient of variation (CV), asymmetric index), local variables (CV2, CV for a sequence of two ISIs; IR, difference of log of two adjacent ISIs; LVr, local variation with refractory period), Fano factor (FF) and Allan factor (AF), and three new burst metrics, 'burst percentage' (BP), 'burst tendency' (BT) and 'burst entropy' (BE), were extensively tested on representative simulated spike trains. Upon verifying that individual metrics could not by themselves reliably classify the diverse simulation patterns, a novel tri-component classification algorithm was developed. Inadequate metrics were rejected and the remaining selected metrics were grouped and weighted using multiple metric optimization to form three proxy metrics: 'regularity' (combining local variables), 'burstiness' (combining BP, BT and BE), and 'corruption' (combining FF and AF). RESULTS: The accuracy of the proxy metrics was verified on a large set of neuronal spike trains extracellularly recorded from multiple regions of the brain in unsedated normal and dystonic rats. Cross-validation of the tri-component classifier against meticulous subjective classification of these data demonstrated an agreement of 95.9%, with a high discriminatory power of 2.6. COMPARISON WITH EXISTING METHODS: The tri-component classifier was demonstrated to well outperform individual metrics on all aspects of pattern and corruption discrimination. CONCLUSIONS: The tri-component classifier provides a novel, reliable algorithm to differentiate highly diverse neuronal discharge patterns and discriminate natural or erroneous corruption in the signal.